Project: Domain Workflow Assistant
Pattern: C (AI-Native System)
Timeline: 10-12 weeks
Users: Primary operators + end users
Context: Reduce repetitive manual work by 40% while maintaining task quality

[User type] faces [problem] when [situation].
This causes [negative outcome].
We know this because [evidence].

Primary operators face long response times when end users request recurring workflow help. This causes user dissatisfaction and operator burnout handling repetitive inquiries.

We know this because:
- 60% of requests are recurring "How do I..." questions
- Average response time is 4 hours
- Operator surveys show 70% of time spent on repetitive questions
- NPS dropped from 45 to 38 in past 6 months

Primary Metric:
- Reduce support ticket volume by 40% within 3 months of launch

Secondary Metrics:
- 80% of common questions answered by AI without escalation
- <2 second response time for AI answers
- >4.0/5.0 user satisfaction rating with AI responses
- 50% reduction in agent time spent on common questions

Minimum Success:
- 30% ticket reduction + 4.0/5.0 satisfaction

When [situation], I want to [action], so I can [outcome].

End-User Stories:
1. When I have a billing question, I want instant answers, so I can resolve issues without waiting.
2. When I'm setting up my account, I want step-by-step guidance, so I don't get stuck.
3. When I need to reset my password, I want a simple self-service flow, so I don't need to contact support.

Agent Stories:
1. When a complex issue arrives, I want context from the AI conversation, so I can help efficiently.
2. When training new agents, I want the AI to handle basics, so I can focus on teaching advanced topics.
3. When end users escalate, I want interaction history, so I don't ask redundant questions.

P0 (Core - MVP):
- FR-001: System answers common questions from knowledge base
- FR-002: System escalates to human when confidence is low (<70%)
- FR-003: Agents can see full conversation history
- FR-004: System tracks conversation satisfaction ratings

P1 (Important - Post-MVP):
- FR-005: System learns from agent corrections
- FR-006: System handles multi-turn conversations with context
- FR-007: Agents can override AI suggestions

P2 (Nice-to-have - Future):
- FR-008: System proactively suggests help articles
- FR-009: System detects dissatisfied end users
- FR-010: Multi-language support

Performance:
- NFR-001: Response time <2 seconds for 95th percentile
- NFR-002: Handle 100 concurrent conversations
- NFR-003: Knowledge base search <500ms

Security:
- NFR-004: End-user data encrypted at rest and in transit
- NFR-005: SOC2 Type II compliance
- NFR-006: Role-based access control (RBAC)
- NFR-007: Audit logs for all AI responses

Scalability:
- NFR-008: Support 10,000 conversations/day at launch
- NFR-009: Scale to 100,000 conversations/day within 6 months

Reliability:
- NFR-010: 99.9% uptime SLA
- NFR-011: Graceful degradation if AI service unavailable

Usability:
- NFR-012: Agents can use with <10 minutes training
- NFR-013: WCAG 2.1 AA accessibility compliance

Integrations:
- Must integrate with the existing system of record
- Must use the organization's approved identity provider
- Must emit logs and metrics to the existing observability platform

Technology Stack:
- Backend: Approved backend runtime for the project
- AI model: Approved model provider or self-hosted model
- Retrieval/index layer: Approved search, database, or vector index
- Frontend: Existing UI framework or platform standard

Infrastructure:
- Deploy on the existing hosting or runtime environment
- Use existing CI/CD and release pipelines

Budget:
- AI/service usage budget: TBD monthly maximum
- Infrastructure budget: TBD monthly maximum

Timeline:
- MVP must launch within 10 weeks
- Full feature set within 16 weeks

Data Sources:
- Knowledge base articles (500+ source documents)
- Historical workflow records (2 years)
- Product or process documentation
- Public or internal FAQ pages

Data Models:
- Interactions: id, end_user_id, operator_id, messages[], status, satisfaction_rating
- Messages: id, sender, text, timestamp, ai_confidence
- Knowledge: id, title, content, embeddings, category, last_updated

Data Privacy:
- PII must be redacted before AI processing
- Conversation data retained for 90 days
- Analytics data aggregated and anonymized
- GDPR right-to-delete compliance

Data Security:
- Encrypt end-user data at rest (AES-256)
- Encrypt in transit (TLS 1.3)
- Role-based access to conversation data

End-User Interface:
- Chat widget in bottom-right corner
- Typing indicators and response time estimates
- Clear "Talk to a human" button always visible
- Conversation history accessible for 30 days

Operator Interface:
- Side panel showing AI suggestions
- One-click "Accept AI answer" button
- Edit AI answer before sending
- Flag incorrect responses for retraining
- Dashboard showing AI performance metrics

User Flows:
1. End user asks question → System retrieves answer → Displays with confidence
2. Low confidence → Auto-escalate to operator → Operator sees full context
3. Operator corrects response → System logs correction for improvement

Design Constraints:
- Match existing brand colors
- Mobile-responsive design
- WCAG 2.1 AA compliant
- Support keyboard navigation

Risks:
1. AI hallucination risk
   - Mitigation: Confidence thresholds, human review for low confidence

2. Knowledge base quality risk
   - Mitigation: Content audit before launch, SME review of top 100 articles

3. User adoption risk (operators do not rely on assisted output)
   - Mitigation: Gradual rollout, agent training, show accuracy metrics

4. API cost overruns
   - Mitigation: Aggressive caching, token limits, usage monitoring

Assumptions:
1. End users will accept assisted responses (validate with beta test)
2. Knowledge base is accurate and up-to-date (audit required)
3. 80% of questions can be answered with existing knowledge
4. Approved AI/service latency is acceptable (<2s)

Dependencies:
1. Access to system-of-record API (approval required)
2. Knowledge base export from source repository
3. AI/service quota increase if current limits are insufficient
4. Operator availability for training and feedback

Out of Scope for MVP:
- Voice/phone support integration (post-MVP)
- Multi-language support (Phase 2)
- Integration with CRM system (future)
- Custom AI model training unless explicitly required
- Mobile app (web only initially)
- Proactive workflow initiation (on-demand assistance only)
- Sentiment analysis dashboard (future analytics)
- Agent performance scoring (v2 feature)

Explicitly NOT Building:
- Custom LLM training (too expensive)
- Real-time translation (complexity vs. value)
- Video call integration (different project)